论文标题
我们可以在艺术作品中检测和谐吗？机器学习方法
Can we detect harmony in artistic compositions? A machine learning approach
论文作者
论文摘要
视觉组成中的和谐是一个概念，即使是人类也无法在数学上定义或容易表达。本文描述的研究的目的是找到具有不同和谐级别的艺术作品的数值表示。我们要求人类根据他们传达的和谐的灰度图像进行评分。为了表示图像，设计和提取了一组特殊功能。通过这样做，可以将客观措施分配给主观判断的作品。鉴于评分和提取的功能，我们利用机器学习算法来评估和谐分类问题中此类表示的效率。最佳性能模型（SVM）在区分谐波和不和谐图像方面达到了80％的准确性，这加强了一个假设，即和谐概念可以以数学方式表达，该概念可以由人类评估。
Harmony in visual compositions is a concept that cannot be defined or easily expressed mathematically, even by humans. The goal of the research described in this paper was to find a numerical representation of artistic compositions with different levels of harmony. We ask humans to rate a collection of grayscale images based on the harmony they convey. To represent the images, a set of special features were designed and extracted. By doing so, it became possible to assign objective measures to subjectively judged compositions. Given the ratings and the extracted features, we utilized machine learning algorithms to evaluate the efficiency of such representations in a harmony classification problem. The best performing model (SVM) achieved 80% accuracy in distinguishing between harmonic and disharmonic images, which reinforces the assumption that concept of harmony can be expressed in a mathematical way that can be assessed by humans.
